Effective One-Dimensional Models from Matrix Product States

In this paper we present a method for deriving effective one-dimensional models based on the matrix product state formalism. It exploits translational invariance to work directly in the thermodynamic limit. We show, how a representation of the creation operator of single quasi-particles in both real and momentum space can be extracted from the dispersion calculation. The method is tested for the analytically solvable Ising model in a transverse magnetic field. Properties of the matrix product representation of the creation operator are discussed and validated by calculating the one-particle contribution to the spectral weight. Results are also given for the ground state energy and the dispersion.Comment: 17 pages, 8 figure

Communication Analysis through Visual Analytics: Current Practices, Challenges, and New Frontiers

The automated analysis of digital human communication data often focuses on specific aspects such as content or network structure in isolation. This can provide limited perspectives while making cross-methodological analyses, occurring in domains like investigative journalism, difficult. Communication research in psychology and the digital humanities instead stresses the importance of a holistic approach to overcome these limiting factors. In this work, we conduct an extensive survey on the properties of over forty semi-automated communication analysis systems and investigate how they cover concepts described in theoretical communication research. From these investigations, we derive a design space and contribute a conceptual framework based on communication research, technical considerations, and the surveyed approaches. The framework describes the systems' properties, capabilities, and composition through a wide range of criteria organized in the dimensions (1) Data, (2) Processing and Models, (3) Visual Interface, and (4) Knowledge Generation. These criteria enable a formalization of digital communication analysis through visual analytics, which, we argue, is uniquely suited for this task by tackling automation complexity while leveraging domain knowledge. With our framework, we identify shortcomings and research challenges, such as group communication dynamics, trust and privacy considerations, and holistic approaches. Simultaneously, our framework supports the evaluation of systems and promotes the mutual exchange between researchers through a structured common language, laying the foundations for future research on communication analysis.Comment: 11 pages, 2 tables, 1 figur

Effective one-dimensional models including two-particle interaction from matrix product states

In this thesis a method for deriving effective models for one-dimensional spin systems is introduced. It is based on matrix product state (MPS) and exploits translation invariance to efficiently work in the thermodynamic limit. It is tested on two analytically solvable models: The ferromagnetic spin-\textonehalf\ Heisenberg chain in an external field, and the transverse magnetic field Ising model (TFIM). The previously developed ansatz for one-particle states is extended to the description of two-particle states. The challenges of this extension and different choices for a basis of the two-particle space are discussed. Results for the two-particle spectral weight in the TFIM and for quasi-particle scattering in both models are provided

Comparison of the iterated equation of motion approach and the density matrix formalism for the quantum Rabi model

The density matrix formalism and the equation of motion approach are two semi-analytical methods that can be used to compute the non-equilibrium dynamics of correlated systems. While for a bilinear Hamiltonian both formalisms yield the exact result, for any non-bilinear Hamiltonian a truncation is necessary. Due to the fact that the commonly used truncation schemes differ for these two methods, the accuracy of the obtained results depends significantly on the chosen approach. In this paper, both formalisms are applied to the quantum Rabi model. This allows us to compare the approximate results and the exact dynamics of the system and enables us to discuss the accuracy of the approximations as well as the advantages and the disadvantages of both methods. It is shown to which extent the results fulfill physical requirements for the observables and which properties of the methods lead to unphysical results

VulnEx : Exploring Open-Source Software Vulnerabilities in Large Development Organizations to Understand Risk Exposure

The prevalent usage of open-source software (OSS) has led to an increased interest in resolving potential third-party security risks by fixing common vulnerabilities and exposures (CVEs). However, even with automated code analysis tools in place, security analysts often lack the means to obtain an overview of vulnerable OSS reuse in large software organizations. In this design study, we propose VulnEx (Vulnerability Explorer), a tool to audit entire software development organizations. We introduce three complementary table-based representations to identify and assess vulnerability exposures due to OSS, which we designed in collaboration with security analysts. The presented tool allows examining problematic projects and applications (repositories), third-party libraries, and vulnerabilities across a software organization. We show the applicability of our tool through a use case and preliminary expert feedback.publishe

ParSetgnostics: Quality Metrics for Parallel Sets

While there are many visualization techniques for exploring numeric data, only a few work with categorical data. One prominent example is Parallel Sets, showing data frequencies instead of data points - analogous to parallel coordinates for numerical data. As nominal data does not have an intrinsic order, the design of Parallel Sets is sensitive to visual clutter due to overlaps, crossings, and subdivision of ribbons hindering readability and pattern detection. In this paper, we propose a set of quality metrics, called ParSetgnostics (Parallel Sets diagnostics), which aim to improve Parallel Sets by reducing clutter. These quality metrics quantify important properties of Parallel Sets such as overlap, orthogonality, ribbon width variance, and mutual information to optimize the category and dimension ordering. By conducting a systematic correlation analysis between the individual metrics, we ensure their distinctiveness. Further, we evaluate the clutter reduction effect of ParSetgnostics by reconstructing six datasets from previous publications using Parallel Sets measuring and comparing their respective properties. Our results show that ParSetgostics facilitates multi-dimensional analysis of categorical data by automatically providing optimized Parallel Set designs with a clutter reduction of up to 81% compared to the originally proposed Parallel Sets visualizations

HistoBankVis : Detecting Language Change via Data Visualization

We present HistoBankVis, a novel visualization system designed for the interactive analysis of complex, multidimensional data to facilitate historical linguistic work. In this paper, we illustrate the visualization’s efficacy and power by means of a concrete case study investigating the diachronic interaction of word order and subject case in Icelandic.publishe

Exploring the Design Space of BioFabric Visualization for Multivariate Network Analysis

The visual analysis of multivariate network data is a common yet difficult task in many domains. The major challenge is to visualize the network’s topology and additional attributes for entities and their connections. Although node-link diagrams and adjacency matrices are widespread, they have inherent limitations. Node-link diagrams struggle to scale effectively, while adjacency matrices can fail to represent network topologies clearly. In this paper, we delve into the design space of BioFabric, which aligns entities along rows and relationships along columns, providing a way to encapsulate multiple attributes for both. We explore how we can leverage the unique opportunities offered by BioFabric’s design space to visualize multivariate network data — focusing on three main categories: juxtaposed visualizations, embedded on-node and on-edge encoding, and transformed node and edge encoding. We complement our exploration with a quantitative assessment comparing BioFabric to adjacency matrices. We postulate that the expansive design possibilities introduced in BioFabric network visualization have the potential for the visualization of multivariate data, and we advocate for further evaluation of the associated design space. Our supplemental material is available on osf.io